Generates the following sequential two-arm experimental designs: (1) completely randomized (Bernoulli) (2) balanced completely randomized (3) Efron's (1971) Biased Coin (4) Atkinson's (1982) Covariate-Adjusted Biased Coin (5) Kapelner and Krieger's (2014) Covariate-Adjusted Matching on the Fly (6) Kapelner and Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights (Naive) (7) Kapelner and Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights (Stepwise) and also provides the following types of inference: (1) estimation (with both Z-style estimators and OLS estimators), (2) frequentist testing (via asymptotic distribution results and via employing the nonparametric randomization test) and (3) frequentist confidence intervals (only under the superpopulation sampling assumption currently). Details can be found in our publication: Kapelner and Krieger "A Matching Procedure for Sequential Experiments that Iteratively Learns which Covariates Improve Power" (2020) <arXiv:2010.05980>.

Seq2pathway is a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data, consisting of "seq2gene" and "gene2path" components. The seq2gene links sequence-level measurements of genomic regions (including SNPs or point mutation coordinates) to gene-level scores, and the gene2pathway summarizes gene scores to pathway-scores for each sample. The seq2gene has the feasibility to assign both coding and non-exon regions to a broader range of neighboring genes than only the nearest one, thus facilitating the study of functional non-coding regions. The gene2pathway takes into account the quantity of significance for gene members within a pathway compared those outside a pathway. The output of seq2pathway is a general structure of quantitative pathway-level scores, thus allowing one to functional interpret such datasets as RNA-seq, ChIP-seq, GWAS, and derived from other next generational sequencing experiments.

These Rcpp'-based functions compute the efficient score statistics for grouped time-to-event data (Prentice and Gloeckler, 1978), with the optional inclusion of baseline covariates. Functions for estimating the parameter of interest and nuisance parameters, including baseline hazards, using maximum likelihood are also provided. A parallel set of functions allow for the incorporation of family structure of related individuals (e.g., trios). Note that the current implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to departures from model assumptions, and should be considered experimental. For these data, the exact proportional-hazards-model-based likelihood is computed by evaluating multiple variable integration. The integration is accomplished using the Cuba library (Hahn, 2005), and the source files are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, 2002).

Fast, optimal, and reproducible clustering algorithms for circular, periodic, or framed data. The algorithms introduced here are based on a core algorithm for optimal framed clustering the authors have developed (Debnath & Song 2021) <doi:10.1109/TCBB.2021.3077573>. The runtime of these algorithms is O(K N log^2 N), where K is the number of clusters and N is the number of circular data points. On a desktop computer using a single processor core, millions of data points can be grouped into a few clusters within seconds. One can apply the algorithms to characterize events along circular DNA molecules, circular RNA molecules, and circular genomes of bacteria, chloroplast, and mitochondria. One can also cluster climate data along any given longitude or latitude. Periodic data clustering can be formulated as circular clustering. The algorithms offer a general high-performance solution to circular, periodic, or framed data clustering.

Computes the extended spring indices (SI-x) and false spring exposure indices (FSEI). The SI-x indices are standard indices used for analysis in spring phenology studies. In addition, the FSEI is also from research on the climatology of false springs and adjusted to include an early and late false spring exposure index. The indices include the first leaf index, first bloom index, and false spring exposure indices, along with all calculations for all functions needed to calculate each index. The main function returns all indices, but each function can also be run separately. Allstadt et al. (2015) <doi: 10.1088/1748-9326/10/10/104008> Ault et al. (2015) <doi: 10.1016/j.cageo.2015.06.015> Peterson and Abatzoglou (2014) <doi: 10.1002/2014GL059266> Schwarz et al. (2006) <doi: 10.1111/j.1365-2486.2005.01097.x> Schwarz et al. (2013) <doi: 10.1002/joc.3625>.

Detect feedback loops (cycles, circuits) between species (nodes) in ordinary differential equation (ODE) models. Feedback loops are paths from a node to itself without visiting any other node twice, and they have important regulatory functions. Loops are reported with their order of participating nodes and their length, and whether the loop is a positive or a negative feedback loop. An upper limit of the number of feedback loops limits runtime (which scales with feedback loop count). Model parametrizations and values of the modelled variables are accounted for. Computation uses the characteristics of the Jacobian matrix as described e.g. in Thomas and Kaufman (2002) <doi:10.1016/s1631-0691(02)01452-x>. Input can be the Jacobian matrix of the ODE model or the ODE function definition; in the latter case, the Jacobian matrix is determined using numDeriv'. Graph-based algorithms from igraph are employed for path detection.

This package contains the functions for construction and visualization of underlying and reflexivity graphs of the three families of the proximity catch digraphs (PCDs), see (Ceyhan (2005) ISBN:978-3-639-19063-2), and for computing the edge density of these PCD-based graphs which are then used for testing the patterns of segregation and association against complete spatial randomness (CSR)) or uniformity in one and two dimensional cases. The PCD families considered are Arc-Slice PCDs, Proportional-Edge (PE) PCDs (Ceyhan et al. (2006) <doi:10.1016/j.csda.2005.03.002>) and Central Similarity PCDs (Ceyhan et al. (2007) <doi:10.1002/cjs.5550350106>). See also (Ceyhan (2016) <doi:10.1016/j.stamet.2016.07.003>) for edge density of the underlying and reflexivity graphs of PE-PCDs. The package also has tools for visualization of PCD-based graphs for one, two, and three dimensional data.

The C++ header files of the Stan project are provided by this package. There is a shared object containing part of the CVODES library, but it is not accessible from R. r-stanheaders is only useful for developers who want to utilize the LinkingTo directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies.

The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or variational methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, templated statistical and linear algebra functions that can handle the automatically differentiable scalar types (and doubles, ints, etc.), and a parser for the Stan language. The r-rstan package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.

This package provides easy access to essential climate change datasets to non-climate experts. Users can download the latest raw data from authoritative sources and view it via pre-defined ggplot2 charts. Datasets include atmospheric CO2, methane, emissions, instrumental and proxy temperature records, sea levels, Arctic/Antarctic sea-ice, Hurricanes, and Paleoclimate data. Sources include: NOAA Mauna Loa Laboratory <https://gml.noaa.gov/ccgg/trends/data.html>, Global Carbon Project <https://www.globalcarbonproject.org/carbonbudget/>, NASA GISTEMP <https://data.giss.nasa.gov/gistemp/>, National Snow and Sea Ice Data Center <https://nsidc.org/home>, CSIRO <https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/>, NOAA Laboratory for Satellite Altimetry <https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/> and HURDAT Atlantic Hurricane Database <https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html>, Vostok Paleo carbon dioxide and temperature data: <doi:10.3334/CDIAC/ATG.009>.

Tool for easy prior construction and visualization. It helps to formulates joint prior distributions for variance parameters in latent Gaussian models. The resulting prior is robust and can be created in an intuitive way. A graphical user interface (GUI) can be used to choose the joint prior, where the user can click through the model and select priors. An extensive guide is available in the GUI. The package allows for direct inference with the specified model and prior. Using a hierarchical variance decomposition, we formulate a joint variance prior that takes the whole model structure into account. In this way, existing knowledge can intuitively be incorporated at the level it applies to. Alternatively, one can use independent variance priors for each model components in the latent Gaussian model. Details can be found in the accompanying scientific paper: Hem, Fuglstad, Riebler (2024, Journal of Statistical Software, <doi:10.18637/jss.v110.i03>).

Org-Babel support for evaluating rust code. Much of this is modeled after `ob-C'. Just like the `ob-C', you can specify :flags headers when compiling with the "rust run" command. Unlike `ob-C', you can also specify :args which can be a list of arguments to pass to the binary. If you quote the value passed into the list, it will use `ob-ref to find the reference data. If you do not include a main function or a package name, `ob-rust will provide it for you and it's the only way to properly use very limited implementation: - currently only support :results output. ; Requirements: - You must have rust and cargo installed and the rust and cargo should be in your `exec-path rust command. - rust-script - `rust-mode is also recommended for syntax highlighting and formatting. Not this particularly needs it, it just assumes you have it.

Perform inference in the secondary analysis setting with linked data potentially containing mismatch errors. Only the linked data file may be accessible and information about the record linkage process may be limited or unavailable. Implements the General Framework for Regression with Mismatched Data developed by Slawski et al. (2023) <doi:10.48550/arXiv.2306.00909>. The framework uses a mixture model for pairs of linked records whose two components reflect distributions conditional on match status, i.e., correct match or mismatch. Inference is based on composite likelihood and the Expectation-Maximization (EM) algorithm. The package currently supports Cox Proportional Hazards Regression (right-censored data only) and Generalized Linear Regression Models (Gaussian, Gamma, Poisson, and Logistic (binary models only)). Information about the underlying record linkage process can be incorporated into the method if available (e.g., assumed overall mismatch rate, safe matches, predictors of match status, or predicted probabilities of correct matches).

Org Ref is an Emacs library that provides rich support for citations, labels and cross-references in Org mode.

The basic idea of Org Ref is that it defines a convenient interface to insert citations from a reference database (e.g., from BibTeX files), and a set of functional Org links for citations, cross-references and labels that export properly to LaTeX, and that provide clickable functionality to the user. Org Ref interfaces with Helm BibTeX to facilitate citation entry, and it can also use RefTeX.

It also provides a fairly large number of utilities for finding bad citations, extracting BibTeX entries from citations in an Org file, and functions to create and modify BibTeX entries from a variety of sources, most notably from a DOI.

Org Ref is especially suitable for Org documents destined for LaTeX export and scientific publication. Org Ref is also useful for research documents and notes.

stJoincount facilitates the application of join count analysis to spatial transcriptomic data generated from the 10x Genomics Visium platform. This tool first converts a labeled spatial tissue map into a raster object, in which each spatial feature is represented by a pixel coded by label assignment. This process includes automatic calculation of optimal raster resolution and extent for the sample. A neighbors list is then created from the rasterized sample, in which adjacent and diagonal neighbors for each pixel are identified. After adding binary spatial weights to the neighbors list, a multi-categorical join count analysis is performed to tabulate "joins" between all possible combinations of label pairs. The function returns the observed join counts, the expected count under conditions of spatial randomness, and the variance calculated under non-free sampling. The z-score is then calculated as the difference between observed and expected counts, divided by the square root of the variance.

Included are two main interfaces, bentcable.ar() and bentcable.dev.plot(), for fitting and diagnosing bent-cable regressions for autoregressive time-series data (Chiu and Lockhart 2010, <doi:10.1002/cjs.10070>) or independent data (time series or otherwise - Chiu, Lockhart and Routledge 2006, <doi:10.1198/016214505000001177>). Some components in the package can also be used as stand-alone functions. The bent cable (linear-quadratic-linear) generalizes the broken stick (linear-linear), which is also handled by this package. Version 0.2 corrected a glitch in the computation of confidence intervals for the CTP. References that were updated from Versions 0.2.1 and 0.2.2 appear in Version 0.2.3 and up. Version 0.3.0 improved robustness of the error-message producing mechanism. Version 0.3.1 improves the NAMESPACE file of the package. It is the author's intention to distribute any future updates via GitHub.

This package provides a large number of measurements generate count data. This is a statistical data type that only assumes non-negative integer values and is generated by counting. Typically, counting data can be found in biomedical applications, such as the analysis of DNA double-strand breaks. The number of DNA double-strand breaks can be counted in individual cells using various bioanalytical methods. For diagnostic applications, it is relevant to record the distribution of the number data in order to determine their biomedical significance (Roediger, S. et al., 2018. Journal of Laboratory and Precision Medicine. <doi:10.21037/jlpm.2018.04.10>). The software offers functions for a comprehensive automated evaluation of distribution models of count data. In addition to programmatic interaction, a graphical user interface (web server) is included, which enables fast and interactive data-scientific analyses. The user is supported in selecting the most suitable counting distribution for his own data set.

Continuous glucose monitoring (CGM) systems provide real-time, dynamic glucose information by tracking interstitial glucose values throughout the day. Glycemic variability, also known as glucose variability, is an established risk factor for hypoglycemia (Kovatchev) and has been shown to be a risk factor in diabetes complications. Over 20 metrics of glycemic variability have been identified. Here, we provide functions to calculate glucose summary metrics, glucose variability metrics (as defined in clinical publications), and visualizations to visualize trends in CGM data. Cho P, Bent B, Wittmann A, et al. (2020) <https://diabetes.diabetesjournals.org/content/69/Supplement_1/73-LB.abstract> American Diabetes Association (2020) <https://professional.diabetes.org/diapro/glucose_calc> Kovatchev B (2019) <doi:10.1177/1932296819826111> Kovdeatchev BP (2017) <doi:10.1038/nrendo.2017.3> Tamborlane W V., Beck RW, Bode BW, et al. (2008) <doi:10.1056/NEJMoa0805017> Umpierrez GE, P. Kovatchev B (2018) <doi:10.1016/j.amjms.2018.09.010>.

In tumor tissue, underlying genomic instability can lead to DNA copy number alterations, e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the genome, whereas recurrent alterations are observed in the same genomic region across multiple independent samples, perhaps because they provide a selective growth advantage. Here we use cyclic shift permutations to identify recurrent copy number alterations in a single cohort or recurrent copy number differences in two cohorts based on a common set of genomic markers. Additional functionality is provided to perform downstream analyses, including the creation of summary files and graphics. DiNAMIC.Duo builds upon the original DiNAMIC package of Walter et al. (2011) <doi:10.1093/bioinformatics/btq717> and leverages the theory developed in Walter et al. (2015) <doi:10.1093/biomet/asv046>. An article describing DiNAMIC.Duo by Walter et al. (2022) can be found at <doi: 10.1093/bioinformatics/btac542>.

Tests whether the linear hypothesis of a model is correct specified using Dominguez-Lobato test. Also Ramsey's RESET (Regression Equation Specification Error Test) test is implemented and Wald tests can be carried out. Although RESET test is widely used to test the linear hypothesis of a model, Dominguez and Lobato (2019) proposed a novel approach that generalizes well known specification tests such as Ramsey's. This test relies on wild-bootstrap; this package implements this approach to be usable with any function that fits linear models and is compatible with the update() function such as stats'::lm(), lfe'::felm() and forecast'::Arima(), for ARMA (autoregressiveâ moving-average) models. Also the package can handle custom statistics such as Cramer von Mises and Kolmogorov Smirnov, described by the authors, and custom distributions such as Mammen (discrete and continuous) and Rademacher. Manuel A. Dominguez & Ignacio N. Lobato (2019) <doi:10.1080/07474938.2019.1687116>.

This package provides methods for model selection, model averaging, and calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned income data where the topmost bin is right-censored. We provide both a non-parametric method, termed the bounded midpoint estimator (BME), which assigns cases to their bin midpoints; except for the censored bins, where cases are assigned to an income estimated by fitting a Pareto distribution. Because the usual Pareto estimate can be inaccurate or undefined, especially in small samples, we implement a bounded Pareto estimate that yields much better results. We also provide a parametric approach, which fits distributions from the generalized beta (GB) family. Because some GB distributions can have poor fit or undefined estimates, we fit 10 GB-family distributions and use multimodel inference to obtain definite estimates from the best-fitting distributions. We also provide binned income data from all United States of America school districts, counties, and states.

Analyzing genetic data obtained from pooled samples. This package can read in Fragment Analysis output files, process the data, and score peaks, as well as facilitate various analyses, including cluster analysis, calculation of genetic distances and diversity indices, as well as bootstrap resampling for statistical inference. Specifically tailored to handle genetic data efficiently, researchers can explore population structure, genetic differentiation, and genetic relatedness among samples. We updated some functions from Covarrubias-Pazaran et al. (2016) <doi:10.1186/s12863-016-0365-6> to allow for the use of new file formats and referenced the following to write our genetic analysis functions: Long et al. (2022) <doi:10.1038/s41598-022-04776-0>, Jost (2008) <doi:10.1111/j.1365-294x.2008.03887.x>, Nei (1973) <doi:10.1073/pnas.70.12.3321>, Foulley et al. (2006) <doi:10.1016/j.livprodsci.2005.10.021>, Chao et al. (2008) <doi:10.1111/j.1541-0420.2008.01010.x>.

This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage.

This package performs the permutation test using difference in the restricted mean survival time (RMST) between groups as a summary measure of the survival time distribution. When the sample size is less than 50 per group, it has been shown that there is non-negligible inflation of the type I error rate in the commonly used asymptotic test for the RMST comparison. Generally, permutation tests can be useful in such a situation. However, when we apply the permutation test for the RMST comparison, particularly in small sample situations, there are some cases where the survival function in either group cannot be defined due to censoring in the permutation process. Horiguchi and Uno (2020) <doi:10.1002/sim.8565> have examined six workable solutions to handle this numerical issue. It performs permutation tests with implementation of the six methods outlined in the paper when the numerical issue arises during the permutation process. The result of the asymptotic test is also provided for a reference.

Access Datastream content through <https://product.datastream.com/dswsclient/Docs/Default.aspx>., our historical financial database with over 35 million individual instruments or indicators across all major asset classes, including over 19 million active economic indicators. It features 120 years of data, across 175 countries â the information you need to interpret market trends, economic cycles, and the impact of world events. Data spans bond indices, bonds, commodities, convertibles, credit default swaps, derivatives, economics, energy, equities, equity indices, ESG, estimates, exchange rates, fixed income, funds, fundamentals, interest rates, and investment trusts. Unique content includes I/B/E/S Estimates, Worldscope Fundamentals, point-in-time data, and Reuters Polls. Alongside the content, sit a set of powerful analytical tools for exploring relationships between different asset types, with a library of customizable analytical functions. In-house timeseries can also be uploaded using the package to comingle with Datastream maintained datasets, use with these analytical tools and displayed in Datastreamâ s flexible charting facilities in Microsoft Office.